Decision Space Decomposition for Multiobjective Programs
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ProQuest Dissertations & Theses
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Soriano, Emma Kari | |
| 245 | 1 | |a Decision Space Decomposition for Multiobjective Programs | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Being inspired by the parametric decomposition theorem for multiobjective optimization problems (MOPs) of Cuenca and Miguel (2017), and by the block-coordinate descent for single objective optimization problems, we present a decomposition theorem for computing the set of minimal elements of a partially ordered set. This set is decomposed into subsets whose minimal elements are used to retrieve the overall minimal elements. We apply this approach to strictly convex MOPs decomposing their decision space into lines. The line decomposition benefits from the fact that a multiobjective line search problem is equivalent to solving a collection of single objective line search problems. In the presence of one objective function, no modifications of the method are needed. We implement this decomposition algorithm in Python for bi-objective and single-objective programs with bounded variables. We prove the convergence of this algorithm and provide preliminary error analysis for an implementation in R n . | |
| 653 | |a Decomposition | ||
| 653 | |a Partial differential equations | ||
| 653 | |a Python | ||
| 653 | |a Error analysis | ||
| 653 | |a Decision making | ||
| 653 | |a Computer engineering | ||
| 653 | |a Computer science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254023341/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254023341/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |